Is it possible for us to truly understand what life really is
-- and how it works, in general, not only for a few special examples?

Back
at the time of Charles Darwin, before his revolutionary new ideas became
popular, biologists often seemed to be doomed to a life of meaningless
leaf-collecting. They seemed to be collecting a gigantic archive of raw,
descriptive data – without being able to generalize or to predict or to
understand the underlying patterns. This has changed a lot since then -- but
even today we face a big challenge in trying to generalize and understand all
the data which has been accumulated.

Is
it even possible to develop a kind of unified, mathematical understanding of
life in general? Is it even possible to develop the same kind of
universal, coherent mathematical understanding here as it is in the case of
physics or in the case of intelligent systems? Is it just plain inherently
intractable – or are there ways to deepen our understanding that we
simply haven’t yet discovered? Can we develop an understanding general
enough, for example, to actually predict what kinds of life might exist
in other chemistries or other hypothetical universes? Or even what kind of life
we might accidentally create on earth? Can we even be sure, theoretically,
about what kinds of scenarios from science about alternative life forms would
actually be possible, in some environments? (I have wondered at times about
Philip Jose Farmer’s scenario in To Your Scattered Bodies Go.)

Maybe
such an understanding is impossible – but before we give up, we should
think about how impossible it once seemed to develop a unifying mathematical
understanding of basic physics or of intelligence. The first great unifying
mathematical understanding in physics came from Newton. It was incomplete, but it was a huge
step forward – and it led toever more complete and advanced understanding unrolling over centuries.
In the study of mind or intelligence, we have really only just begun that
process – but there certainly is new, unifying mathematics, and the
emerging new technology that comes form greater understanding. In the study of
“what is life?”, there are number of interesting strands waiting to
be woven together more coherently… and mathematical approaches yet to be
explored.

Life,
in the most general context, is really just one part of the larger subject of self-organization
or self-organizing systems. Back in 1994, Karl Pribram invited Ilya Prigogine,
myself and a couple of dozen others to address the basic questions about
self-organization in one of his workshops. He invited me to write a general
introduction to self-organization, which was printed next to Prigogine’s
paper in the start of the book:

I have not had time to really focus on this area since then,
but I have time to muse a bit on some of the key questions. I have wondered:
can we somehow unify our understanding of life with whatever can be learned
from general-purpose thermodynamics, nonlinear system dynamics, and artificial
life and so on? Could the mathematics developed for intelligent systems be of
some use here, if only to suggest more orderly chains of approximation or ways
to handle genomic/proteomic data? Von Neumann certainly included this area as
one of the three big areas he focused on, in trying to develop new mathematics
to help us better understand the foundations of everything.

NSF for many years ran a small activity called “Quantitative
Systems Biology,” which comes the closest I know of to providing a
venue for answering these kinds of questions. The Engineering perspective is
extremely important here, as in the study of intelligent systems, because it
stimulates us to think about the possibilities for life in general, not
just the particular list of life forms we happen to have access to right now.

Greg Bear’s books about Darwin’s children
– fictional as it is in many ways – acutely poses a related
question here: to what extent is the “junk DNA,” 97% of the genome,
actually a kind of learning system, like the brain. More precisely, how much of
the genome is intended to help us learn how to choose better gene
expressions, as opposed to merely specifying the final “actions” at
the output layer of the “gene brain”?Certainly “metalearning” is
a crucial unmet challenge to evolutionary computing, as well as to more brain-like approaches to nonconvex function maximization.
Metalearning is only one of several theories about the function of the
“junk DNA” being discussed in the the new International Postgenetics Society. It
seems very odd to me that even today there are many who truly believe that
“junk DNA” has no function at all – that 97% of the genome is
all just a matter of gross inefficiency and accidental waste!

Would it be possible to use approaches more like quantum
statistical thermodynamics

(like the master equations so familiar in quantum optics) or
even simple eigenvector and

growth analysis, to try to solve for the forms of life that
emerge as simple or unicellular

patterns in a homogeneous fluid environment? Would the early
Russian work on autocatalytic reactions help point the way here? (Of course,
autocatalysis, aging and

death are likely to be important even to the most general
initial models…)

In the field of history, many
theorists have argued that a “frontier” – a high rate of growth,
as one finds in a kind of open system – is essential to maintaining high
civilization, altruism, freedom, progress, etc. Yet an interesting aspect of
sociobiology is that this parameter – the degree of growth of the entire
population – is not really such an important driver as often thought.
George Gaylord Simpson has described related, but more complete, versions of
the frontier concept. But in the end… there are many parameters which
come into it. The stable historic pastoral societies which E.O. Wilson
describes sounds like a crucial ingredient to some kind of progress…
until one imagines turning them into tribes in Afghanistan carrying nuclear
weapons and growing opium.